Predictive Maintenance in critical infrastructure is a fundamental tool for predicting a failure in advance and for avoiding catastrophic equipment damage that can be prevented and the time-consuming repair scheduling can be executed in time. Artificial Intelligence (AI) based predictive maintenance utilises intelligent data for accurate predictions in order to make immediate interventions on critical assets. In this paper, we propose a 5G-enabled Network Application (NetApp) for predictive maintenance in energy-related critical infrastructures. The proposed NetApp consists of several containerised components responsible for retrieving time-series operational data from a power plant and detecting potential outliers/anomalies regarding the operation of energy generators. For the anomaly detection process, an autoencoder is used. The evaluation results demonstrate the efficiency of the proposed NetApp.